Recent advancements in the field of video surveillance systems, machine vision systems in the field of robotics and automotive industry, and consumer electronic (CE) devices is largely due to rapid technological development in image processing techniques. One of such image processing techniques is image segmentation that may refer to partitioning of an image into several regions based on certain rules. Although various segmentation methods have been known to separate foreground objects from background of an image, the complexity, accuracy, and computational resource requirement varies based on an objective to be achieved. For example, it may be comparatively simple to segment a foreground object with prior knowledge of the shape or size of the foreground object. In certain scenarios, without knowing the size, shape, and/or the number of colors related to a foreground object, the segmentation becomes a challenging problem. Further, the problem is increased manifold if there are multiple objects with same or similar size, shape, and/or colors in an image, and one of them needs to be segmented. In such scenarios, current image segmentation approaches may either fail to segment or output a segmented foreground object with a rough boundary, which may not be visually appealing to a viewer. Therefore, an improved technique and system may be required for fast object boundary smoothening for precise segmentation of a foreground object.
Further limitations and disadvantages of conventional and traditional approaches will become apparent to one of skill in the art, through comparison of described systems with some aspects of the present disclosure, as set forth in the remainder of the present application and with reference to the drawings.